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Ab Initio Simulations on the Structure and Properties of Supported Core-Shell Pt Nanoparticles on Single-Layer MoS2

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Abstract

To effectively employ core-shell nanoparticles in catalysis and sensing necessarily requires a deep atomic-level understanding of their structural and electronic properties. Moreover, for both applications, nanoparticles are usually deposited over a support material which is known to lead to significant (and often unpredictable) changes in their properties and stability. In this work, we study a number of model Pt core-shell nanoparticles supported on single-layer molybdenum disulfide (MoS2). Our aim is to investigate the effect of the support on the properties of such nanoparticles, and we do so by means of first-principles simulations in the framework of density functional theory (DFT). Here, we show that the stability and the catalytic and/or sensing properties of the supported nanoparticles correlate with both strain and charge transfer, which are concurring and competing effects. Overall, our results suggest that core-shell Pt clusters supported on MoS2 may be successfully used in catalysis and in field-effect biosensing.

Original languageEnglish
Pages (from-to)24666-24675
Number of pages10
JournalJournal of Physical Chemistry C
Volume127
Issue number51
DOIs
Publication statusPublished - 28 Dec 2023

Bibliographical note

Publisher Copyright:
© 2023 American Chemical Society.

Funding

The authors acknowledge funding from the European Union’s Horizon 2020 research and innovation programme, EU H2020 SmartVista project ( www.smartvista.eu ), grant agreement no. 825114. This work was granted access to the HPC/AI resources of IDRIS (Institut du Développement et des Ressources en Informatique Scientifique) under the allocation 2021-A0110811060 made by GENCI (Grand Équipement National de Calcul Intensif). The authors acknowledge funding from the European Union’s Horizon 2020 research and innovation programme, EU H2020 SmartVista project (www.smartvista.eu), grant agreement no. 825114. This work was granted access to the HPC/AI resources of IDRIS (Institut du Développement et des Ressources en Informatique Scientifique) under the allocation 2021-A0110811060 made by GENCI (Grand Équipement National de Calcul Intensif).

FundersFunder number
European Union's Horizon 2020 - Research and Innovation Framework Programme825114

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